Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

Aggarwal, Shruti; Suchithra, M.; Chandramouli, N.; Sarada, Macha; Vetrithangam, D.; Pant, Bhaskar; Adugna, Biruk Ambachew

doi:10.1155/2022/1757888

Cited by 25 publications

(8 citation statements)

References 35 publications

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“…In (Aggarwal et al 2022), reviews of agreed businesses designed for accuracy widening the rice formation considered to be the main harvests on the earth were analyzed. Also, an overview and investigation of several materials and methods recognized with crop disease identification were also introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Crop Disease Using Rank Regressive Learning and Proaftn Fuzzy Classification Models

jayabrabhu¹,

Thangaraja²

2023

Preprint

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Today, Machine Learning (ML) is driving the big variation covering significant industries. Agriculture is one industry where ML researchers are operating with farmers to assist farmers put together a better and more significant utilization of the dwindling resources owing to the metropolitan and big data concept. Nevertheless, plant disease, specifically crop plants, is an extensive menace as far as global food security is concerned. Diseases prevalent in plants at the first hand influence the fruit or grain quality, therefore resulting in the lessening of agricultural fertility. The conventional method of crop disease identification is performed via visual investigation. However, this type of process is said to be extremely incompetent and hence susceptible to the error with the inception of big data. Over the past few years, several works on ML techniques for crop disease prediction have been proposed. To overcome this proposed work, a novelty method called Rank Regressive Learning and Proaftn Fuzzy Classification - crop disease prediction with big data for soybean is proposed. The RRL-PFC crop disease prediction method is split into two sections. They are feature selection and crop disease prediction. Initially, raw data is obtained from the Soybean dataset (i.e., one of the crops utilized from crop yield), that are produced to input vector matrix. To choose the relevant features that are designed for categorizing crop disease, the Rank Regressive learning-based Feature Selection is applied than vector matrix as input. Finally, with the computationally efficient selected features, categorization of crop disease is made by means of Proaftn Fuzzy Classification-based Crop Disease Prediction in an accurate manner. The experimental evaluation of the proposed RRL-PFC method with respect to improved accuracy and lesser time, overhead, and error rate than the conventional methods.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Crop Disease Using Rank Regressive Learning and Proaftn Fuzzy Classification Models

jayabrabhu¹,

Thangaraja²

2023

Preprint

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“…It divides two classes by drawing a hyperplane. By introducing hyperplanes, the vectors can be isolated into different classes [24]. Many models have been developed using SVM.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…At that point, a component vector is constructed and forwarded to the classifier (for perceiving the paddy leaf ailments). The precision of the model was 92% [24]. Another Framework was developed using SVM.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Chakraborty,

Banerjee,

Samanta

et al. 2023

IJECC

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Rice is one of the most important staple food crops in the world. Most Asian countries are dependent on rice and huge quantities of rice are grown every year. However, there are many categories of diseases (e.g., blast) which affect rice production and can ultimately lead to huge financial loss to rice growers. Yield loss due to rice blast disease about 10 to 30 percent annually and under favourable condition, this disease can destroy the rice plant within 15 to 20 days and cause yield loss up to 100%.Therefore to ensure better quality, quantity and better productivity early disease detection should be done so that the right amount of pesticides can be as administered at right time to curb the infection. Nowadays Machine Learning has been integrated into the agriculture sector. The aim of this review paper is to identify which Machine Learning algorithms work best in rice blast disease detection. The algorithms reviewed here include Naive Bayes, LSTM RNN, Random Forest Classifiers, Support Vector Machines, K Means, Decision Tree and Convolutional Neural Networks. This review paper also covers the future scope of improvement of some Machine Learning algorithms like Naive Bayes and Recurrent Neural Networks.

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“…A detailed review of AI and ML methods for rice disease detection is performed in [23]. They review various methods in AI, ML, and even deep learning strategies for rice disease recognition due to the importance of the rice plant globally.…”

Section: Recent Studiesmentioning

confidence: 99%

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Latif

Abdelhamid

Mallouhy

et al. 2022

Plants

110

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Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

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Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

Cited by 25 publications

References 35 publications

Prediction of Crop Disease Using Rank Regressive Learning and Proaftn Fuzzy Classification Models

Prediction of Crop Disease Using Rank Regressive Learning and Proaftn Fuzzy Classification Models

Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

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